Heart failure monitoring and reduction of respiration induced under sensing of cardiac events

ABSTRACT

Computer implemented methods, systems and devices are provided to monitor for potential heart failure (HF). Cardiac activity (CA) data is obtained and filtered to obtain respiration data indicative of a respiration pattern. The respiration data is analyzed to determine one or more respiration characteristics of interest (COI) that are recorded along with collection time information to form an HF monitoring log. Additionally or alternatively, the CA data is analyzed to detect an event of interest. The cardiac activity data is filtered to obtain respiration data indicative of a respiration pattern, and the respiration data is analyzed for respiration induced under detection of the event of interest from the CA data.

REFERENCE TO RELATED APPLICATIONS

The present application is a divisional application of, and claimspriority to, U.S. application Ser. No. 15/803,596, Titled “HEART FAILUREMONITORING AND REDUCTION OF RESPIRATION INDUCED UNDER SENSING OF CARDIACEVENTS” which was filed on 3 Nov. 2017, the complete subject matter ofwhich is expressly incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

Embodiments herein relate generally to implantable medical devices, andmore particularly to implantable loop recorders for monitoring cardiacevents such as heart rate and rhythm.

BACKGROUND OF THE INVENTION

Implantable cardiac monitors (ICM) is a small medical device that isimplanted in a location to continuously monitor heart rhythms and recordelectrocardiograms (ECGs) automatically or with patient activation. AnICM uses electrodes placed at the distal and proximal end of the deviceto sense rhythms wirelessly at an orientation and location. The purposeof ICM is to help clinicians diagnose for, and treat, abnormal heartactivities that can be either asymptomatic or cause symptoms such asseizures, recurrent palpitations (noticeably rapid, strong, or irregularheartbeats due to agitation, exertion, or illness), lightheadedness,dizziness, or syncope (fainting). These abnormal heart activitiesinclude bradycardia (slow heart rate), tachycardia (fast heart rate),asystole (no electrical heart activity), atrial or ventriculararrhythmias (problems with rate or rhythm of heart beat), and evenatrial fibrillation (AF; very fast or irregular heart beat).

However, ICMs can exhibit false detection of cardiac arrhythmias underinappropriate R wave sensing. Several variables, like respiration, cancause the ICM to inappropriately detect heart signals such as byaltering R-wave amplitudes and morphologies. For example, existingalgorithms used by ICMs for detecting arrhythmias are primarily based onthe irregularity of R-waves. The respiration signal modulates R-waveamplitudes. A breath occurs over a respiratory cycle that includes oneinspiration phase (inhalation) and one expiration phase (exhalation). Asa patient breathes, the chest expands and contracts, which causes theICM to move relative to the heart. The lungs inflate and deflate,resulting in alternating increases and decreases in an amplitude of anECG signal detected by the ICM. In other words, as lungs expand duringinhalation, the ICM is pushed away from the heart. The change inheart-electrode distance results in a change in the signals measured bythe electrodes at the ICM. In addition, the conductivity of lungs, whendeflated, is about three times higher than when lungs are inflated, suchthat electric potentials that reach electrodes at the ICM also changeduring respiration. Patients have been observed to exhibit both anincrease and decrease of R-wave amplitudes during a breathing test. Thepotential exists that the ICM detects heart beats simultaneous with thestrong respiration artifact, thereby resulting in an inappropriatedetection by the ICM. For example, the ICM may detect a normal heartrhythm as bradycardia or asystole, and/or the ICM may under-detect atachycardia.

SUMMARY

In accordance with embodiments herein, a computer implemented method isprovided to monitor for potential heart failure (HF). The method isunder control of one or more processors that are configured withspecific executable instructions. The method obtains cardiac activitydata for multiple cardiac cycles, filters the cardiac activity data toobtain respiration data indicative of a respiration pattern overmultiple respiration cycles, and analyzes the respiration data todetermine one or more respiration characteristics of interest (COI). Themethod further records the respiration COI along with collection timeinformation concerning when the CA data was obtained and repeats theoperations at a) to d) to form an HF monitoring log that includes acollection of respiration COI over a period of time.

Optionally, the method determines a secondary index based on secondarydata, the secondary data representing at least one of heart sounds,activity data or posture data; and records the secondary index in the HFmonitoring log with the corresponding respiration COI. Optionally, themethod may determine at least one of an activity index based on activitydata or a posture index based on posture data. The method may record theat least one of activity index or posture index in the HF monitoring logwith the corresponding respiration COI. The method may analyze thecollection of respiration COI over the period of time with respect tobaseline COI and may generate an indicator of potential heart failurebased on a relation between the collection of respiration COI andbaseline COI.

Optionally, the method may maintain a counter of a number of themultiple respiration cycles for which the respiration COI exceeds thecorresponding baseline COI and may generate the indicator of potentialheart failure when the counter exceeds a count threshold. The method mayselect between first and second baseline COI based on at least one of anactivity index or a posture index. The filtering operation may apply alow pass filter to the CA data to obtain the respiration data.

In accordance with embodiments herein, a system is provided to monitorfor potential heart failure (HF). The system comprises at least oneprocessor and a memory coupled to the at least one processor. The memorystores program instructions. The program instructions are executable bythe at least one processor to obtain cardiac activity data for multiplecardiac cycles, filter the cardiac activity data to obtain respirationdata indicative of a respiration pattern, analyze the respiration datato determine one or more respiration characteristics of interest (COI),record the respiration COI along with collection time informationconcerning when the CA data was obtained and repeat the operations at a)to d) to form an HF monitoring log that includes a collection ofrespiration COI over a period of time.

Optionally, an implantable device may have a housing that encloses thememory and the at least one processor. The implantable device mayinclude a transceiver to wirelessly transmit the HF monitoring log to anexternal device. The implantable device may comprise a sensor that maybe configured to obtain at least one of activity data or posture data.The at least one processor may be configured to determine at least oneof an activity index based on the activity data or a posture index basedon the posture data. The at least one processor may be configured torecord the at least one of activity index or posture index in the HFmonitoring log with the corresponding respiration COI.

Optionally, the at least one processor may be configured to analyze thecollection of respiration COI over the period of time with respect tobaseline COI. The processor may generate an indicator of potential heartfailure based on a relation between the collection of respiration COIand baseline COI. An external device may have a transceiver that may beconfigured to receive the HF monitoring log and may have at least oneprocessor configured to analyze the collection of respiration COI withrespect to baseline COI to generate an indicator of potential heartfailure based on a relation between the collection of respiration COIand baseline COI.

In accordance with embodiments herein a computer implemented method isprovided for identifying respiration induced under sensing of cardiacevents. The method is under control of one or more processors configuredwith specific executable instructions and obtains cardiac activity (CA)data for a cardiac cycle. The method detects whether an event ofinterest is present in the CA data, filters the cardiac activity data toobtain respiration data indicative of a respiration pattern, andanalyzes the respiration data for respiration induced under detection ofthe event of interest from the CA data.

Optionally, the method may classify the cardiac cycle as abnormalcardiac activity when the event of interest was not detected as presentin the CA data. The analyzing operation may include confirming ordenying abnormal cardiac activity based on the respiration data. Thefiltering and analyzing operations may be performed when the detectingoperation fails to detect the event of interest in the CA data. Themethod may obtain the respiration data from the CA data and maydetermine a respiration COI from the respiration data.

Optionally, the analyzing operation may further determine whether therespiration COI exceeds a threshold for a predetermined number ofrespiration cycles. The respiration COI may represent at least one of apeak to peak variation in the respiration data, a frequency of therespiration data, another indicators of tidal volume or morphology ofthe respiration data. The analyzing operation may compare therespiration data and the CA data for respiration induced filtering ofthe event of interest. Optionally the detecting operation detectswhether the event of interest is present in the CA data based on asensitivity threshold, the method further comprising adjusting thesensitivity threshold based on the respiration data.

In accordance with embodiments herein, an implantable cardiac monitordevice is provided. The device comprises an electrode that is configuredto obtain cardiac activity (CA) data for a cardiac cycle. A CA sensingcircuit is configured to detect whether an event of interest is presentin the CA data. A low pass filter (LPF) circuit is configured to filterthe CA data to obtain respiration data indicative of a respirationpattern. The device further comprises at least one processor and amemory coupled to the at least one processor. The memory stores programinstructions. The program instructions are executable by the at leastone processor to analyze the respiration data for respiration inducedunder detection of the event of interest from the CA data.

Optionally, the at least one processor may be configured to adjustsensing parameters of the CA sensing circuit based on the respirationdata. The at least one processor may be configured to adjust the sensingparameters by switching between first and second thresholds applied tothe CA data. The device may further comprise a physiologic sensorcircuit that may be configured to detect at least one of activity orposture data. The at least one processor may be configured to identifyunder detection of the event of interest based on the at least one ofactivity or posture data.

Optionally, the CA sensing circuit detects whether the event of interestis present in the CA data based on a sensitivity threshold, the at leastone processor configured to identify under detection of the event ofinterest based on the respiration data, and in response thereto, adjustthe sensitivity threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an implantable cardiac rhythm monitoring (ICM) deviceintended for subcutaneous implantation at a site near the heart inaccordance with embodiments herein.

FIG. 2A illustrates a block diagram of an exemplary ICM that isconfigured to be implanted into the patient in accordance withembodiments herein.

FIG. 2B illustrates examples of cardiac activity data and respiratorydata, respectively that may be obtained in accordance with embodimentsherein.

FIG. 2C illustrates an example of cardiac activity data and potentialrelated event markers that may be labeled and mis-labeled without therespiration induced under sending processes described herein.

FIG. 2D illustrates example frequency spectrums that may be present inconnection with CA data and respiration data in accordance withembodiments herein.

FIG. 3A illustrates a computer implemented process monitoring forindications of heart failure in accordance with embodiments herein.

FIG. 3B illustrates graphs showing a relation between variouscharacteristics of heart activity.

FIG. 4 illustrates a section of memory that stores information inconnection with the operations of FIG. 3A in accordance with embodimentsherein.

FIG. 5 illustrates a process for detecting potential heart failure basedon respiration data collected over time in accordance with embodimentsherein.

FIG. 6 illustrates a process for utilizing respiration data to reducefalse detection of abnormal cardiac activity in accordance withembodiments herein.

FIG. 7 illustrates a process utilizing the respiratory data to guideadjustment in the sensing parameters in real-time for the CA data inaccordance with embodiments herein.

FIG. 8 illustrates a system level diagram indicating potential devicesand networks in which the methods and systems herein may be utilized inaccordance with embodiments herein.

FIG. 9 illustrates a functional block diagram of the external devicethat is operated in accordance with the processes described herein andto interface with ICMs as described herein in accordance withembodiments herein.

FIG. 10 illustrates a distributed processing system in accordance withone embodiment herein.

DETAILED DESCRIPTION

The terms “abnormal cardiac activity” and “abnormal cardiac episodes”include, but are not limited to, bradycardia (slow heart rate),tachycardia (fast heart rate), asystole (no electrical heart activity),atrial or ventricular arrhythmias (problems with rate or rhythm of heartbeat), and even atrial fibrillation (AF; very fast or irregular heartbeat).

The terms “under sense”, “under detect” and “under detection” refer tothe condition in which the device fails to sense true cardiac activity.For example, cardiac data may be recorded that includes a QRS complexrelated to a normal/physiologic event, but the QRS complex may have arelatively small peak to peak amplitude. When the peak to peak amplitudefalls below a predetermined threshold, the ICM may not recognize the QRScomplex as a normal R-wave and will not count or label the R-wave as anormal event, thereby leaving an under-sensed gap in the cardiacactivity.

The terms “false event detection” and “false detection” refer toincorrect identification and labeling (or lack of any detection orlabeling) of an event. For example, an ICM may trigger a falsebradycardia detection if one or more true QRS complex are under sensed.

In accordance with embodiments herein, ICM are provided to monitorpatients for causes of abnormal heart activities such as potentialepisodes of bradycardia/asystole causing syncope, possible AF episodescausing cryptogenic stroke, potential episodes of atrial or ventriculararrhythmias causing intermittent chest palpitations and the like.Further, ICMs described herein monitor for heart failure (HF). The ICMdetects electrical signal originated from the heart and attenuated bythe lungs. The ICM is positioned within the body at a location above theheart such that, when the heart generates electrical signals, thesignals pass through the lungs before reaching the ICM. In accordancewith embodiments herein, the ICM collects cardiac activity (CA) datafrom ICM sensing electrodes. The detected CA data is low pass filteredto extract respiration data. The ICM analyzes the respiration data alongwith other data, such as activity and posture dada. For instance,respiration data may show increases directly or inversely proportionalto increases in detected cardiac data amplitudes depending on verticalor horizontal patient position which is detected from a posture sensor.The changes in respiration data such as peak to peak values or otherindicators of tidal volume can be used to monitor HF status.

Embodiments herein avoid high occurrences of false event detection thatmay otherwise occur due to factors, including respiration. Embodimentsherein use low pass filtering to detect a size of respiration, which canbe used to guide a search for the cause of false event detection alongwith other sensors such as activity and posture sensors. The respirationdata can also be used to adjust sensing parameters.

FIG. 1 illustrates an implantable cardiac rhythm monitoring (ICM) device10 intended for subcutaneous implantation at a site near the heart 12.The monitoring device includes a pair of spaced-apart sense electrodes14 positioned with respect to a housing 16. The sense electrodes 14provide for detection of far field electrogram signals. Numerousconfigurations of electrode arrangements are possible. For example, theelectrodes 14 may be located on the same side of the housing 16.Alternatively, the electrodes 14 may be located on opposite sides of thehousing 16. One of the electrodes 14 may be formed as part of thehousing 16, for example, by coating all but a portion of the housingwith a nonconductive material such that the uncoated portion forms theelectrode. In this case, the other of the electrodes 14 may beelectrically isolated from the housing electrode by placing it on acomponent separate from the housing, such as a header (not shown). Inother configurations, the electrodes 14 may be located on short, stubleads extending away from the housing but coupled thereto through one ormore headers so as to interface with internal components. The housing 16includes various other components such as: sense electronics forreceiving signals from the electrodes, a microprocessor for processingthe signals in accordance with algorithms, such as the detection ofabnormal cardiac activity, a loop memory for temporary storage ofelectrograms, a device memory for long-term storage of electrograms uponcertain triggering events, sensors for detecting patient activity and abattery for powering components.

The monitoring device 10 senses far field, subcutaneous electrograms,processes the electrograms to detect arrhythmias and if an arrhythmia isdetected, automatically records the electrograms in memory forsubsequent transmission to an external device 18. Electrogram processingand arrhythmia detection is provided for, at least in part, byalgorithms embodied in the microprocessor.

FIG. 2A shows a block diagram of an exemplary ICM 102 (such as device10) that is configured to be implanted into the patient. The ICM 102 isconfigured to monitor atrial activity, ventricular activity, or bothventricular and atrial activity through sensing circuitry. The ICM 102has a housing 100 to hold the electronic/computing components. Thehousing 100 (which is often referred to as the “can”, “case”,“encasing”, or “case electrode”) may be program mably selected to act asan electrode for certain sensing modes. The housing 100 further includesa connector (not shown) with at least one terminal 103 and preferably asecond terminal 104. The terminals 103, 104 may be coupled to sensingelectrodes that are provided upon or immediately adjacent the housing100. Optionally, more than two terminals 103, 104 may be provided inorder to support more than two sensing electrodes to support a truebipolar sensing scheme using the housing as a reference electrode.Additionally or alternatively, the terminals 103, 104 may be connectedto one or more leads having one or more electrodes provided thereon,where the electrodes are located in various locations about the heart.The type and location of each electrode may vary.

The ICM 102 is configured to be placed subcutaneously utilizing aminimally invasive approach. Subcutaneous electrodes are provided on thehousing 100 to simplify the implant procedure and eliminate a need for atransvenous lead system. The sensing electrodes may be located onopposite sides of the device and designed to provide robust episodedetection through consistent contact at a sensor-tissue interface. TheICM 102 may be configured to be activated by the patient orautomatically activated, in connection with recording subcutaneous ECGsignals. The ICM 102 includes a programmable microcontroller 120 thatcontrols various operations of the ICM 102, including cardiacmonitoring. Microcontroller 120 includes a microprocessor (or equivalentcontrol circuitry), RAM and/or ROM memory, logic and timing circuitry,state machine circuitry, and I/O circuitry. The microcontroller 120 alsoperforms the operations described herein in connection with collectingcardiac activity data and analyzing the cardiac activity data toidentify respiration data and respiration COls.

A switch 126 is optionally provided to allow selection of differentelectrode configurations under the control of the microcontroller 120.The electrode configuration switch 126 may include multiple switches forconnecting the desired electrodes to the appropriate I/O circuits,thereby facilitating electrode programmability. The switch 126 iscontrolled by a control signal 128 from the microcontroller 120.Optionally, the switch 126 may be omitted and the I/O circuits directlyconnected to the housing 100 and a second electrode 103. Microcontroller120 includes an HF monitor 132, an arrhythmia detector 134, and arespiration detector 136. The HF monitor 132 populates the HF monitorlog and performs HF operations as described herein. The arrhythmiadetector 134 is configured to analyze cardiac activity data and identifypotential episodes, such as bradycardia or asystole causing syncope,possible AF episodes causing cryptogenic stroke (insufficient blood flowto the brain to meet metabolic demand), potential episodes of atrial orventricular arrhythmias causing intermittent chest palpitations and thelike. By way of example, the arrhythmia detector 134 may implement adetection algorithm as described in U.S. Pat. No. 8,135,456, thecomplete subject matter of which is incorporated herein by reference.The Respiration detector 136 is configured to analyze respirationactivity data as explained herein. Although not shown, themicrocontroller 120 may further include other dedicated circuitry and/orfirmware/software components that assist in monitoring variousconditions of the patient's heart and managing pacing therapies.

The ICM 102 is further equipped with a communication modem(modulator/demodulator) 140 to enable wireless communication. In oneimplementation, the communication modem 140 uses high frequencymodulation, for example using RF or Blue Tooth telemetry protocols. Thesignals are transmitted in a high frequency range and will travelthrough the body tissue in fluids without stimulating the heart or beingfelt by the patient. The communication modem 140 may be implemented inhardware as part of the microcontroller 120, or as software/firmwareinstructions programmed into and executed by the microcontroller 120.Alternatively, the modem 140 may reside separately from themicrocontroller as a standalone component. The modem 140 facilitatesdata retrieval from a remote monitoring network. The modem 140 enablestimely and accurate data transfer directly from the patient to anelectronic device utilized by a physician.

The ICM 102 includes sensing circuitry 144 selectively coupled to one ormore electrodes that perform sensing operations, through the switch 126to detect cardiac activity data indicative of cardiac activity. Thesensing circuitry 144 detects events of interest from the cardiacactivity data utilizing, among other things, a sensitivity threshold. Asexplained herein, the sensitivity threshold may be adjusted when the ICM102 determines that the sensing circuitry 144 is under sensing events ofinterest. The sensing circuitry 144 may include dedicated senseamplifiers, multiplexed amplifiers, or shared amplifiers. It may furtheremploy one or more low power, precision amplifiers with programmablegain and/or automatic gain control, bandpass filtering, and thresholddetection circuit to selectively sense the cardiac signal of interest.In one embodiment, switch 126 may be used to determine the sensingpolarity of the cardiac signal by selectively closing the appropriateswitches.

The output of the sensing circuitry 144 is connected to themicrocontroller 120 which, in turn, determines when to store the cardiacactivity data (digitized by the ND data acquisition system 150) in thememory 160. For example, the microcontroller 120 may only store thecardiac activity data (from the ND data acquisition system 150) in thememory 160 when a potential arrhythmia episode is detected. The sensingcircuitry 144 receives a control signal 146 from the microcontroller 120for purposes of controlling the gain, threshold, polarization chargeremoval circuitry (not shown), and the timing of any blocking circuitry(not shown) coupled to the inputs of the sensing circuitry. In theexample of FIG. 2A, a single sensing circuit 144 is illustrated.Optionally, the ICM 102 may include multiple sensing circuits, similarto sensing circuit 144, where each sensing circuit is coupled to two ormore electrodes and controlled by the microcontroller 120 to senseelectrical activity detected at the corresponding two or moreelectrodes. The sensing circuit 144 may operate in a unipolar sensingconfiguration (e.g., housing 100 to electrode 103) or in a bipolarsensing configuration (e.g., between electrodes 103 and 104 referencedto the housing electrode 100). Optionally, the sensing circuit 144 maybe removed entirely and the microcontroller 120 perform the operationsdescribed herein based upon the EGM signals from the A/D dataacquisition system 150 directly coupled to the electrodes 100, 103,and/or 104.

The sensing circuit 144 includes input lines that carry the cardiacactivity data sensed by the electrodes. The input lines of the sensingcircuit 144 are joined at nodes 145 to inputs of a low pass filter (LPF)circuit 147. The LPF circuit 147 processes the cardiac activity data asdescribed herein to form respiration data. The output of the LPF circuitis coupled to an A/D converter 149 and outputs the respiration data tothe ND converter 149. The A/D converter 149 converts analog respirationdata into a digital form that is provided to the microcontroller 120 foranalysis and storage in memory 160. The embodiment of FIG. 2Aillustrates the input lines to the sensing circuit 144 and LPF circuit147 to carry analog signals. Optionally, the input lines of the sensingcircuit 144 and LPF circuit 147 may carry digital signals, in which casethe sensing circuit 144 and LPF circuit 147 are configured to processdigital CA data. When the LPF circuit 147 receives digital CA data as aninput, the LPF circuit 147 outputs the respiration data in digital formand, the A/D convert 149 may be omitted.

By way of example, the LPF circuit 147 may be defined to pass a lowfrequency envelope of the CA data. For example, the LPF circuit 147 maybe a Butterworth Low Pass Filter having the following filter parameters:Astop=30 dB, Apass=1 dB, Fpass=0.3 Hz, and Fstop=1 Hz. Optionally,alternative types of filters and filter parameters may be utilized.Further, the microcontroller 120 is coupled to the LPF circuit 147through a feedback line 151 to allow the microcontroller 120 to modifythe filter parameters of the LPF circuit 147 automatically anditeratively during operation (e.g., see FIG. 7).

FIG. 2B illustrates examples of cardiac activity data and respiratorydata, respectively, that may be obtained in accordance with embodimentsherein. The cardiac activity data is illustrated as signal 240 where theamplitude of the CA data varies between −0.14 mV and 0.22 mV. The signal240 extends over a period of time (e.g., several seconds) during whichmultiple cardiac cycles/episodes occur, with each cardiac cycle havingat least one cardiac event of interest. In the example of FIG. 2B, thecardiac events of interest represent intrinsic R waves that are detectedwith different amplitude or peak to peak variations. For example, theR-wave 242 has a peak to peak variation between approximately −0.14 mVand 0.18 mV, while R-wave 244 has a peak to peak variation extendingbetween −0.08 mV and 0.08 mV. The amplitudes of the R-wave within the CAdata vary in a somewhat repeating manner between larger amplitudes, suchas in the region 246, and smaller amplitudes, such as in regions 248,250. One or more of the cardiac events 244 having low peak to peakvariation may not be accurately declared to represent an intrinsiccardiac event. Instead, cardiac events 244 with low peak to peakvariation (e.g., in regions 248, 250) may be “under sensed” andclassified as non-events or abnormal cardiac events, thereby appearingas a non-event gap between normal cardiac events.

As described herein, the CA data is filtered by the LPF circuit 147 toform respiratory data. FIG. 2B illustrates respiratory data as signal260 that follows a generally sinusoidal pattern between a series oflocal peaks (e.g., 268, 270) and local valleys (e.g., 272, 274) thatvary between approximately 0.0 mV and −3.5 mV. It is recognized that thevoltage ranges, negative versus positive voltages and the like may vary.The local peaks for the respiratory data generally correspond to thesegments of the CA data that exhibit low peak to peak variation inconnection with events of interest. For example, the local peaks 268,270 in the respiratory data generally correspond in time to the regions248, 250 in the CA data where relatively small peak to peak variation isexhibited in connection with each R-wave.

In connection with the patient's breathing behavior, the local peaks inthe respiratory data correspond to maximum points in inspiration phasesof breathing cycles (e.g., a transition point between the end ofinhalation and the beginning of exhalation). The local valleys in therespiratory data correspond to minimum points of expiration phases ofbreathing cycles (e.g., a transition point between the end of exhalationand the beginning of inhalation). As explained herein, respirationcharacteristics of interest (COI) are derived from the respiration data(e.g., peak to peak variation, respiration frequency). The respirationcharacteristics of interest are used to determine whether activitywithin the CA data at 240 should be treated as intrinsic cardiac eventsor or abnormal cardiac events.

FIG. 2C illustrates an example of cardiac activity data and potentialrelated event markers that may be labeled and mis-labeled without therespiration induced under sending processes described herein. Thecardiac activity data 280 may be sensed over a select time period. Theevent markers 290 are assigned to each corresponding event of interest(and non-event) from the cardiac activity data 280. The cardiac activitydata 280 includes events of interest, such as events 282-285. Asillustrated in FIG. 2C, events 282-285 exhibit different peak to peakamplitudes, due in part to the influence of patient respiration upon thephysical surroundings of the ICM. The respiration signal modulatesR-wave amplitudes. A breath occurs over a respiratory cycle thatincludes one inspiration phase (inhalation) and one expiration phase(exhalation). As a patient breathes, the chest expands and contracts,which causes the ICM to move relative to the heart. The lungs inflateand deflate, resulting in alternating increases and decreases in anamplitude of an ECG signal detected by the ICM.

Absent implementation of the processes described herein, the ICM maydisregard or under sense the events 283-285. The ICM detects andcorrectly classifies the event 282 as an intrinsic ventricular sensedevent, as denoted by a ventricular sense event 292. However, theamplitudes of the events 283-284 may be too small to be detected (e.g.,do not satisfy a threshold) and labeled as intrinsic ventricular eventsby an ICM, but does not implement the respiration correction processesdescribed herein. When the events 283-284 fall below that detectionthreshold, the events 283-284 are not labeled within the event markersseries 290 (as denoted by the gap 294). Event 285 is detected withsufficient amplitude to be labeled as an intrinsic event by the ICM.However, the gap 294 extends for a period of time, during which the ICMdoes not detect a intrinsic ventricular event. The gap 294 exceeds a R-Rinterval and thus the ICM includes a bradycardia marker 295 within theevent markers 290. It is recognized that alternative marking rules andprocesses may be utilized. The example of FIG. 2C is merely intended toexhibit one example of how an ICM may miss-label events in connectionwith under sensing. As explained herein, at least in accordance with theprocesses of FIGS. 6 and 7, potential under sensing may be identifiedand the events 283-285 may be correctly classified as normal intrinsicventricular sensed events.

FIG. 2D illustrates example frequency spectrums that may be present inconnection with CA data and respiration data. A Fast Fourier Transform220 illustrates the frequency content of CA data, where the frequencycontent extends over a substantial frequency range. A Fast FourierTransform 222 illustrates the frequency content of respiration data thatmay be collected by low-pass filtering the CA data. The frequencycontent of the respiration data appears in a relatively short frequencyband 226, while the frequency content of the CA data includes multiplespikes (e.g., in frequency bands 228-230). The FFT amplitude spectrumclearly shows that the respiration signal frequencies are the lowestfrequencies with significant amplitudes before the highest amplitudes.

Returning to FIG. 2A, the ICM 102 further includes an analog-to-digitalA/D data acquisition system (DAS) 150 coupled to one or more electrodesvia the switch 126 to sample cardiac signals across any pair of desiredelectrodes. The data acquisition system 150 is configured to acquirecardiac electrogram (EGM) signals, convert the raw analog data intodigital data, and store the digital data for later processing and/ortelemetric transmission to an external device 154 (e.g., a programmer,local transceiver, or a diagnostic system analyzer). The dataacquisition system 150 is controlled by a control signal 156 from themicrocontroller 120.

The microcontroller 120 is coupled to a memory 160 by a suitabledata/address bus 162. The programmable operating parameters used by themicrocontroller 120 are stored in memory 160 and used to customize theoperation of the ICM 102 to suit the needs of a particular patient. Suchoperating parameters define, for example, detection rate thresholds,sensitivity, automatic features, arrhythmia detection criteria, activitysensing or other physiological sensors, and electrode polarity, etc.

In addition, the memory 160 stores the cardiac activity data, as well asthe markers and other data content associated with detection ofepisodes. The operating parameters of the ICM 102 may be non-invasivelyprogrammed into the memory 160 through a telemetry circuit 164 intelemetric communication via communication link 166 with the externaldevice 154. The telemetry circuit 164 allows intra-cardiac electrogramsand status information relating to the operation of the ICM 102 (ascontained in the microcontroller 120 or memory 160) to be sent to theexternal device 154 through the established communication link 166. Inaccordance with embodiments herein, the telemetry circuit 164 conveysthe cardiac activity data, markers and other information related toepisodes. By way of example, the external device 154 may represent abedside monitor installed in a patient's home and utilized tocommunicate with the ICM 102 while the patient is at home, in bed orasleep. The external device 154 may be a programmer used in the clinicto interrogate the device, retrieve data and program detection criteriaand other features. The external device 154 may be a device that can becoupled over a network (e.g., the Internet) to a remote monitoringservice, medical network and the like. The external device 154facilitates access by physicians to patient data as well as permittingthe physician to review real-time ECG signals while being collected bythe ICM 102.

The ICM 102 may further include magnet detection circuitry (not shown),coupled to the microcontroller 120, to detect when a magnet is placedover the unit. A magnet may be used by a clinician to perform varioustest functions of the ICM 102 and/or to signal the microcontroller 120that the external device 154 is in place to receive or transmit data tothe microcontroller 120 through the telemetry circuits 164.

The ICM 102 can further include one or more physiologic sensor 170. Suchsensors are commonly referred to (in the pacemaker arts) as“rate-responsive” or “exercise” sensors. The physiological sensor 170may further be used to detect changes in the physiological condition ofthe heart, or diurnal changes in activity (e.g., detecting sleep andwake states). The physiologic sensor 170 is configured to generateactivity data and/or posture data that are passed to the microcontroller120 for analysis and optional storage in the memory 160 in connectionwith the cardiac activity data, markers, episode information and thelike. The physiologic sensor 170 is also configured to sense heartsounds that are used as described herein. While shown as being includedwithin the ICM 102, the physiologic sensor(s) 170 may be external to theICM 102, yet still be implanted within or carried by the patient.Examples of physiologic sensors might include sensors that, for example,activity, temperature, sense respiration rate, pH of blood, ventriculargradient, activity, position/posture, minute ventilation (MV), and soforth.

A battery 172 provides operating power to all of the components in theICM 102. The battery 172 is capable of operating at low current drainsfor long periods of time. The battery 172 also desirably has apredictable discharge characteristic so that elective replacement timecan be detected. As one example, the ICM 102 employs lithium/silvervanadium oxide batteries. The battery 172 may afford various periods oflongevity (e.g., three years or more of device monitoring). In alternateembodiments, the batter 172 could be rechargeable. See for example, U.S.Pat. No. 7,294,108, Cardiac event microrecorder and method forimplanting same, which is hereby incorporated by reference.

The ICM 102 provides a simple to configure data storage option to enablephysicians to prioritize data based on individual patient conditions, tocapture significant events and reduce risk that unexpected events aremissed. The ICM 102 may be programmable pre- and post-trigger eventstorage. For example, the ICM 102 may be automatically activated tostore 9-60 seconds of activity data prior to an event of interest and/orto store 9-60 seconds of post event activity. Optionally, the ICM 102may afford patient triggered activation in which pre-event activity datais stored, as well as post event activity data (e.g., pre-event storageof 1-15 minutes and post-event storage of 30-60 seconds). Optionally,the ICM 102 may afford manual (patient triggered) or automaticactivation for EGM storage. Optionally, the ICM 102 may affordadditional programming options (e.g., asystole duration, bradycardiarate, tachycardia rate, tachycardia cycle count). The amount of EGMstorage may vary based upon the size of the memory 160.

The ICM 102 may provide comprehensive safe diagnostic data reportsincluding a summary of heart rate, in order to assist physicians indiagnosis and treatment of patient conditions. By way of example,reports may include episode-related diagnostics for auto trigger events,episode duration, episode count, episode date/time stamp and heart ratehistograms. The ICM 102 may be configured to be relatively small (e.g.,between 2-10 cc in volume) which may, among other things, reduce risk ofinfection during implant procedure, afford the use of a small incision,afford the use of a smaller subcutaneous pocket and the like. The smallfootprint may also reduce implant time and introduce less change in bodyimage for patients.

Next, various processes are described in connection with embodimentsherein that are performed by one or more of the circuits, processors andother structures illustrated in the figures and described in thespecification. The operations of the processes described herein may beimplemented wholly or in part by one or more circuits and/or processorswithin an ICM, a local external device (e.g., bedside monitor, smartphone, tablet device, clinician programmer, etc.) , a remote server (aspart of a hospital network), and the like. FIG. 3A illustrates acomputer implemented process monitoring for indications of heart failurein accordance with embodiments herein. The operations of FIG. 3A may beimplemented at different times and based on various criteria, such aswhen cardiac activity data has been analyzed by a detection module(e.g., arrhythmia detector 134 in FIG. 2) and a potential episode hasbeen identified. Optionally, the operations of FIG. 3A may beimplemented independent of detection of potential episodes in a currentset of cardiac activity data. For example, the operations may beimplemented at predetermined times based on a programmed schedule, on aperiodic basis, based on patient activity, patient posture and the like.

At 302, one or more processors collect cardiac activity data. Forexample, the CA data may be derived from sensor signals sensed at theelectrodes on the ICM. At 302, the processors may analyze the CA datafor one or more criteria of interest and/or store the CA data (e.g., ina first-in first-out buffer). The processors may be within the ICM, anexternal device or located at a remote server. When the processors areat an external device or remote server, the CA data is transmitted fromthe ICM to the external device and/or remote server.

At 303, the one or more processors determine whether one or morerespiration data collection criteria are satisfied. For example, it maynot be desirable or necessary to collect respiration data at the sametime, or every time, that the processes collect CA data. Instead, one ormore collection criteria may be defined (e.g., preprogrammed orautomatically updated). By way of example, the decision at 303 maydetermine to collect respiration data periodically, along with activityand posture data. For example, the collection criteria may definerespiration data to be collected during scheduled time windows (e.g., atselect times each day or each week, at the top of each hour, when thepatient wakes up, has meals, goes to bed, and the like). Additionally oralternatively, other collection criteria may be defined such as one ormore particular postures and/or activity states. For example, atspecific times of day, the processors may open the scheduled time windowand determine whether the patient is at rest and in a sitting posture.

Additionally or alternatively, the collection criteria may be inresponse to detection of particular types of cardiac activity, posturesand/or physiologic activity. For example, the collection criteria may besatisfied when a patient lays down for a predetermined minimum period oftime, remains standing for a predetermined period of time, undergoesmoderate or high activity for a predetermined period of time, and thelike. Additionally or alternatively, the collection criteria may besatisfied when the ICM detects certain characteristics in the cardiacactivity, such as particular types of episodes, a predetermined numberof cardiac cycles in which a P-wave, R-wave, or QRS complex are notdetected, and the like. Optionally, the decision at 303 may be omittedentirely

At 303, when the collection criteria are not satisfied, flow returns to302 and the IMD continues to collect cardiac activity data.Alternatively, when the collection criteria are satisfied, flow advancesto 304. At 304, the CA data is analyzed to obtain respiration dataindicative of respiration activity of the patient. For example, the CAdata may be passed through a low-pass filter circuit to form arespiration signal of respiration data. The respiration data may begenerated by a hardware low-pass filter circuit that receives analog ordigitized CA signals from the electrodes on the ICM. Optionally, thelow-pass filter circuit may be implemented as a software module withinthe one or more processors of the ICM. For example, the CA data may bedigitized and stored in memory. The processors may apply a low-passfilter circuit to the stored CA data to output respiration data that isalso stored in the memory.

Also, at 304, the respiration data is analyzed for one or morerespiration characteristics of interest. For example, the one or moreprocessors may analyze the respiration data to identify positive andnegative peak values therein and based thereon, calculate one or morepeak to peak respiration values or other indicators of tidal volume. Thepeak to peak respiration values, alone or in combination with thecomplete stream of respiration data, may represent the respirationcharacteristic of interest. Additionally or alternatively, theprocessors may analyze the respiration data for other respirationcharacteristics of interest, such as frequency, morphology and the like.

At 306, the one or more processors collect secondary data, such as heartsound data, activity data and/or posture data. For example, a signaloutput by the physiologic sensors 170 may be analyzed to determine alevel of activity that the patient is undergoing and/or to determine aposture of the patient. As a further example, the output of thephysiologic sensor 170 may be used to collect the heart sound signalsfrom electrical signals that contain acceleration of myocardium mass andblood flow.

At 308, the one or more processors analyze the secondary (e.g., heartsound, activity and/or posture) data to determine one or morecorresponding index (e.g., a heart sound index, an activity index and/ora posture index, respectively). For example, the activity data may becompared to one or more thresholds, with each threshold having anassociated activity index value. Based upon the relation between theactivity data and the thresholds, an activity index may be assignedthereto (e.g., no activity, low activity, medium activity, highactivity). Similarly, the posture data may be compared to one or morethresholds, with each threshold having an associated posture indexvalue. Based on the relation between the posture data and thethresholds, a posture index may be assigned thereto (e.g., standing,sitting, reclining, horizontal).

Optionally, the respiration signals can be combined with heart soundsfor monitoring HF progression. FIG. 3B illustrates graphs showing arelation between various characteristics of heart activity. Among otherthings, FIG. 3B illustrates left ventricular pressure, heart sounds,venous pulse and an electrocardiogram over the phases of the cardiaccycle, The heart sound S1 refers to the first heart sound that mainlyresults from the closing of mitral valves at the beginning ofventricular systole. During start of systole, papillary muscles attachedto the mitral valve leaflets contract via chordae tendineae. In turn,the chordae tendineae abruptly tenses and prevents the backflow of bloodinto lower pressure atrial area. The sudden chordae tendineae tensingand the ventricular squeezing against closed semilunar valve (e.g.,aortic valve) sends blood back toward the left atrium and the mitralvalve closes shut by catching the rushing blood with the leaflets. Insummary, the S1 sound is mainly caused by the prolongation of soundwithin the blood associated with this sudden block of flow reversal. Itis a function of the force of ventricular contraction and the distancebetween the valve leaflets. The vibration and acceleration from the flowthrough the gaps of leaflets may contain different frequency componentsfrom the wall motion which may be filtered out through a filter design.The IMD filters the output of the physiologic sensor 170 to obtain thefrequency component of interest. Additionally or alternatively, the IMDmay process the raw S1 sound, where a peak amplitude of the S1 sound isrelated to sudden rise in pressure from the baseline or thecontractility of ventricles.

The S3 heart sound represents the third heart sound, also known as the“ventricular gallop,” occurs at the beginning of diastole just after theS2 sound when the mitral valve opens, allowing passive filling of theleft ventricle. The S3 sound is produced by the large amount of bloodstriking a very compliant left ventricle and the S3 sound indicatesincreased volume of blood within the ventricle. The presence of an S3sound is often a sign of systolic heart failure, but it may sometimes bea normal finding. An existence of S3 sound can be an important sign ofsystolic heart failure because, in the present setting, the myocardiumis usually overly compliant, resulting in a dilated LV.

In accordance with embodiments herein, the processor of the IMD mayanalyze heart sounds to identify characteristics of interest such as achange in a maximum/peak in the S1 heart sound and/or existence of a S3heart sound. When a heart sound characteristic of interest isidentified, the corresponding secondary index is determined. Forexample, the secondary index may indicate a peak level for S1, apresence of S3, a duration of S3 and the like. Due to the closerelationship between heart sound and hemodynamics of the heart andelectrocardiogram (FIG. 1), heart sound can be used to accesshemodynamics potentially.

At 310, the one or more processors record in memory (e.g., an HFmonitoring log), collection time information (e.g., a time and date atwhich the CA data and respiration data were collected). The HFmonitoring log also records the respiration COI and secondary index(e.g., heart sound index, activity index and posture index). Optionally,the HF monitoring log may also include the CA data, the heart sound dataand/or respiration data.

FIG. 4 illustrates a section of memory (e.g., memory 160 in FIG. 2A)that stores information in connection with the operations of FIG. 3A.The memory 160 may be configured to store cardiac activity data 402 fora predetermined period of time, such as a select number of cardiaccycles. For example, the cardiac activity data 402 may be stored in afirst-in first-out buffer having a length sufficient to record thecardiac activity data for one minute, five minutes, 30 seconds, 50heartbeats, 25 heartbeats, etc. The memory 160 also stores therespiration data 404 that is generated based upon the analysis of thecardiac activity data. The memory 160 also stores the respirationcharacteristics of interest 406 that are determined from the respirationdata 404. For example, the respiration Cal 406 may represent a series ofpeak to peak variations measured between successive peaks in therespiration data 404. When multiple peak to peak values are measured, amaximum peak to peak value may be selected. Additionally oralternatively, an average or another mathematical combination of thepeak to peak values may be determined. Optionally, the respiration COI406 may represent a single peak to peak value.

The memory 160 further stores an activity table 408 that may bepreprogrammed to contain thresholds to designate transitions betweendifferent levels of activity. For example, thresholds AT1-AT3 may beused to distinguish between low, moderate and high activity indexes,respectively. The memory 160 further stores a posture table 410 that maybe preprogrammed to contain thresholds to designate transitions betweendifferent postures. For example, thresholds PT1-PT3 may be used todistinguish between a standing posture, sitting posture and proneposture, respectively. The activity and posture tables 408, 410 arereferenced in connection with the operations of FIG. 3A to determine anactivity and posture index associated with the present cardiac activitydata 402, respiration data 404 and respiration COI 406. In accordancewith the operations of FIG. 3A, at 310, the current respiration Cal,activity index and posture index are recorded in a HF monitoring log412, along with a time/date stamp. Additionally or alternatively, thememory 160 and/or HF monitoring log 412 may store heart sound data,heart sound indexes and the like. The HF monitoring log 412 maintains arecord of changes in the respiration data over time.

Over time, the HF monitoring log 412 is populated with multiple datasets. The data from the HF monitoring log 412 may be transmittedwirelessly from the ICM at select times, such as on a day-to-day basis,weekly, monthly and the like. Optionally, the HF monitoring log 412 maybe wirelessly conveyed to an external device on a demand basis (e.g., inresponse to a patient instruction to the ICM, an instruction from abedside external device, patient controlled external device, clinicianexternal device and the like). The ICM may transmit the data from the HFmonitoring log 412 to a bedside monitoring external device, a smartphone, or other external device that is configured to wirelesslycommunicate with the ICM. The external device may analyze the HFmonitoring log for certain criteria. For example, when pulmonary edemais developed, a size of the respiration signals will decrease due tofluid buildup and overload within the lungs. As the fluid builds up inthe lungs, the two peak values within the respiration data will decreaseover time. Accordingly, a series of successive entries in the HFmonitoring log 412 may exhibit a common type of activity or posture, butwith progressively decreasing peak to peak variations in the respirationdata. When the foregoing pattern is exhibited, methods and systemsherein may designate a potential development of pulmonary edema, and inresponse thereto, direct the patient to take appropriate correctiveactions (e.g., consult a physician, change medications, etc.).

Different patients may exhibit differences in the values and relationsof respiration characteristics of interest in connection with cardiacactivity data. For example, patients with different physicalcharacteristics (weight, height, age, gender) will experiencedifferences in the values and relations of respiration characteristicsof interest with respect to cardiac activity data. In accordance withembodiments herein, baseline respiration information may be collectedfrom individual patients. For example, the operations of FIG. 3A may beperformed once or periodically to collect baseline respiration data fora patient in which a particular ICM is implanted. The baselinerespiration data is used to derive baseline respiration characteristicsof interest (e.g., baseline peak to peak values and the like), relativeto particular postures and/or activity levels. Additionally oralternatively, the baseline information may be collected when a patientundergoes particular postures for a predetermined period of time,changes in posture, activity states and the like. For example, during abaseline establishment phase, each time a patient lays down, the ICM maycollect respiration data and respiration COI. During the baselineestablishment phase, the ICM may collect the respiration data and COIwhen certain activity levels are detected and/or other posture statesare detected. The baseline information may be then combined to formbaselines for each potential posture state (e.g., when standing,sitting, prone, supine, etc.) and/or various potential activity states(e.g., low, moderate, high). The baseline information may be then usedlater, in connection with analyzing data in the HF monitoring log todetermine relative changes of an individual patient over time.

FIG. 5 illustrates a process for detecting potential heart failure basedon respiration data collected over time. The operations of FIG. 5 may beimplemented wholly or in part by one or more circuits and/or processorswithin an ICM, a local external device (e.g., bedside monitor, smartphone, tablet device, clinician programmer, etc.) , a remote server (aspart of a hospital network), and the like. The operations of FIG. 5 maybe implemented based on various criteria, such as when cardiac activitydata has been analyzed by a detection module (e.g., arrhythmia detector134 in FIG. 2) and a potential episode has been identified. Optionally,the operations of FIG. 5 may be implemented independent of detection ofpotential episodes in a current set of cardiac activity data. Forexample, the operations may be implemented at predetermined times basedon a programmed schedule, on a periodic basis, based on patientactivity, patient posture and the like.

At 502, the one or more processors obtain an HF monitoring log andoptionally obtain baseline respiration data and/or COI for a particularpatient. The baseline information may be specific to the patient, ascaptured by an ICM within the patient. Alternatively, the baselineinformation may be generally defined for a patient population (e.g., apatient population having similar gender, height, weight, agecharacteristics). Optionally, the baseline information may be specifiedin other manners (e.g., program by a clinician).

At 504, the one or more processors analyze the respiration COI withrespect to baseline respiration COI. For example, the baselinerespiration COI may represent preprogrammed thresholds for the peak topeak interval, respiration frequency and the like. When the peak to peakinterval and/or respiration frequency/rate cross a correspondingthreshold, this may represent an indication of potential congestiveheart failure. Additionally or alternatively, the baseline respirationCOI may be patient specific, and collected by the ICM as explainedherein. Additionally or alternatively, the baseline respiration COI mayrepresent an acceptable range at which a corresponding characteristicmay vary over time. For example, it may be determined that a baselinepeak to peak interval should only vary within a desired range for anygiven posture, heart sound and activity level. As another example, thebaseline respiration COI may represent an acceptable range for breathingrate/frequency for a given posture and activity state. The analysis at504 may compare the information from the log with the baseline COI and,when a difference there between exceeds acceptable limits/thresholds,the processors may determine that potential congestive heart failure isoccurring.

At 506, the one or more processors determine whether the respiration COIexceeds the corresponding baseline COI. The baseline COI may be selectedin part based on the posture index and/or activity index. For example, afirst baseline COI may be utilized when the activity index indicates lowactivity and/or the posture index indicates a prone position, while asecond baseline COI may be utilized when the activity index indicateshigh activity and/or and the standing position index indicated astanding position. As another example, the baseline COI may utilize apeak threshold for S1 and/or a threshold for S3. When the respirationCOI exceeds the baseline COI, flow moves to 508. Otherwise, the processof FIG. 5 ends. At 508, the one or more processors increment one or morecounters. The counter(s) track the number of times that the respirationdata exceeds a corresponding baseline or threshold. For example, thecount at 508 may count the number of episodes in which the peak to peakinterval in the respiratory data falls below a minimum threshold.Additionally or alternatively, the counter may track the number ofepisodes in which the breathing rate exceeds a minimum threshold thatwould otherwise be expected when the patient is experiencing aparticular level of activity and in a particular posture. At 510, theone or more processors determine whether the counter has exceeded athreshold. When the counter or counters exceed the correspondingthresholds, flow moves to 512. Otherwise, the process of FIG. 5 ends.

At 512, the one or more processors generate an indicator of potentialheart failure. For example, when the peak to peak intervals from therespiration data from the HF monitoring log exhibit more variation thanthe desired range, the one or more processors generate an indication ofpotential congestive heart failure. When the respiration data from theHF monitoring log exhibits a variation in the breathing rate/frequencythat exceeds the ranges threshold, the one or more processors generatean indication of potential congestive heart failure. The indication ofpotential heart failure may represent an audible or vibratory output bythe ICM in a manner to be detected by the patient. Additionally oralternatively, the indicatory may be a message or flag wirelesslytransmitted to an external device (e.g., bedside monitor or smart phone)that then outputs an audible indication to the patient.

The operations of FIG. 5 may be performed periodically, such as toprovide a day-to-day comparison of respiration data collected from thepatient.

FIG. 6 illustrates a process for utilizing respiration data to reducefalse detection of abnormal cardiac activity in accordance withembodiments herein. At 602, one or more processors of the ICM collect CAdata. For example, cardiac activity may be sensed at the electrodes ofthe ICM and processed to form digitized CA data that is stored in memoryof the ICM. At 604, the one or more processors analyze the CA data toidentify and label events of interest and non-event gaps. The events ofinterest and non-event gaps within the CA data are identified andlabeled based on sensing parameters. Non-limiting examples of sensingparameters include gain, sensitivity, thresholds, decay delay, slope andoverall morphology. For example, the CA data may be analyzed by asensing circuit that has a predetermined gain and sensitivity toidentify an event of interest when the signal that is sensed exceeds athreshold within a sensing window. For example, when the CA data exceedsa threshold, during a P-wave sensing window, the event of interest maybe labeled as a P-wave. As another example, when the CA data exceeds athreshold, during an R-wave sensing window, the event of interest to belabeled as an R-wave. As a further example, during an R-wave sensingwindow, the sensing circuit may also compare the CA data to one or morepredetermined slopes or morphologies to identify a QRS complex and thelike. As explained hereafter, one or more of the sensing parameters maybe adjusted based on the respiration data to avoid under sensing ofevents of interest and/or to confirm/deny non-event gaps.

As further examples, in connection with ventricular sensing, theprocessors may identify a first R-wave, but then not detect anotherR-wave within the R-R interval. Optionally, in connection with atrialsensing, the processors may identify a first P-wave, but then not detectanother P-wave within the P-P interval. Optionally, the processors mayidentify a sensed P-wave but then not detect an R-wave within a P-Rinterval. As another option, the processors may not identify an R-wave,but then not detect a P-wave within a corresponding interval. When thecardiac data lacks one or more P-waves and/or R-waves, the processorsmay classify the cardiac cycle(s) as asystole, bradycardia or anotherabnormal cardiac activity.

At 608, the one or more processors determine whether a detection routinehas declared a normal rhythm or abnormal cardiac activity, such as anasystole episode, a bradycardia episode and the like. When abnormalcardiac activity is not declared (e.g., the detection routine detected anormal cardiac cycle), the process returns to 602 where new CA data iscollected. When abnormal cardiac activity is declared, flow continues to610. The operations at 610 to 614 determine whether respiration activityhas caused a false declaration of an abnormal cardiac activity.

At 610, the ICM obtains respiration data and determines one or morerespiration COI from the respiration data. The respiration data may havebeen previously stored in memory at the same time as the CA data, suchas by utilizing a first-in first-out loop buffer that stored a digitizedoutput of a LPF circuit. The LPF circuit may continuously write over oldrespiration data in the buffer. Alternatively, the LPF circuit andbuffer may only be activated periodically when it is desirable to runthe process of FIG. 6. Alternatively, the respiration data may becalculated at 610 by applying a software low pass filter circuit to theCA data collected and stored at 602. The respiration COI is determinedbased on the respiration data in accordance with operations describedherein.

At 612, the one or more processors determine whether the respiration COIexceeds (e.g., above or below) one or more corresponding thresholds forN respiration cycles, where N is 1 or more. For example, the processorsmay determine that peak to peak amplitude of the respiration dataexceeds a threshold for 1 breath, 5 breaths, and the like. Optionally,the processors may determine whether the respiration peak to peakamplitude exceeds the threshold for a number of respiration cycles thatcorresponds to the number of cardiac cycles declared to exhibit abnormalCA (e.g., 3 breathes that correspond to 9 heart beats). If therespiration COI does not exceed the threshold for the desired number ofrespiration cycles, the process determines that respiration did notcause false detection of abnormal CA and the process ends. The ICMrecords the abnormal CA and does not change any other sensingparameters. Alternatively, if the respiration COI exceeds the thresholdfor the select number N respiration cycles, the process continues to614.

At 614, the one or more processors compare the respiration data and CAdata for respiration related filtering of CA events of interest. At 616,the one or more processors determine whether respiration filtering hasinduced under sensing. For example, the processors may identifyrespiration COI (e.g., peaks) in the respiration data and determinewhether the respiration COI align, in time, with in the CA data labeledas non-event gaps. When the CA data includes non-event gaps that formedthe basis for classification of a cardiac cycle and when a predeterminednumber of the non-event gaps align in time with the respiration COI,embodiments herein may determine that respiration filtering is causingotherwise normal CA events to be under sensed.

Returning to the example of FIGS. 2B and 2C, the process of FIG. 6 mayanalyze (at 608) the events 282-285 in the CA data 280. As noted above,the events 283-284 may not be sensed or classified as non-events(corresponding to the gap 294 in the markers 290). With respect to FIG.2B, the process of FIG. 6 may determine (at 612) that the peak to peakamplitudes within the respiration data 260 exceed the correspondingthreshold for a desired number of respiratory cycles. Accordingly, theprocess of FIG. 6 compares (at 614) the respiratory data 260 and the CAdata 240, and determines that the local peaks 268, 270 correspond withsections of the cardiac data 240, for which no intrinsic event ofinterest was identified (e.g., non-event gaps).

Returning to FIG. 6, at 616, when the process determined thatrespiration filtering has not induced under sensing, the process ends.Otherwise, when the process determines that respiration filtering hasinduced under sensing, flow continues to 618. At 618, the one or moreprocessors automatically update one or more sensing parameters. Forexample, the one or more processors may be configured to adjust thesensitivity threshold when the processor(s) identify under detection ofthe event of interest. With reference to FIG. 2A, the microcontroller120 may update one or more sensing parameters of the sensing circuit 144through the control signal 146. By way of example, the microcontroller120 may adjust the gain, sensitivity, thresholds and other sensingparameters. The adjustments to the sensing parameters may be performedin a programmed or automatically determined manner. For example, firstand second thresholds may be preprogrammed, where the sensing circuit isinitially set to use a first threshold to detect a presence of events ofinterest. An event of interest is detected when the CA data exceeds thefirst threshold. No event of interest is detected when the CA data doesnot exceed the first threshold. At 618, the update to the sensingparameter may be to switch to the preprogrammed second (e.g., lower)threshold to be used by the sensing circuit to detect for a presence ofsubsequent event of interest.

Additionally or alternatively, the gain or sensitivity of the sensingcircuit 144 may be automatically adjusted (e.g., increased or decreased)by predetermined increments at each iteration through 618 as the processof FIG. 6 determines that respiration filtering has induced undersensing. As another example, the sensing parameters for detectingcardiac events of interest may correspond to adjustments in a decaydelay, slope or overall morphology of the CA data. At 618, theprocessors may adjust one or more parameters by predetermined orautomatically derived amounts.

Additionally or alternatively, at 618 an output may be generated andsaved to indicate that an event of interest was not correctly detected.Thereafter, a clinician, when reviewing the notification of false eventdetection, may adjust the sending parameters or take other correctiveaction.

FIG. 7 illustrates a process utilizing the respiratory data to guideadjustment in the sensing parameters in real-time for the CA data inaccordance with embodiments herein. At 702, one or more processors ofthe ICM collect CA data. At 704, one or more processors obtainrespiration data from the CA data. At 706, the one or more processorsdetermine one or more respiration COI from the respiration data.

At 708, the one or more processors adjust a CA sensing parameter basedon the respiratory COI. For example, based on the point in therespiratory cycle, the processors may adjust the sensitivity of the CAsensing circuit. For example, the sensing circuit 144 (FIG. 2A) may beadjusted to increase the sensitivity as the respiratory data approacheslocal peaks. The sensing circuit 144 may be adjusted to decrease thesensitivity as the respiratory data approaches local valleys/minimums.Optionally, other parameters of the sensing circuit 144 may be adjustedbased on the point in the respiratory cycle. Optionally, more specifictypes of feedback information may be derived from the respiration datain connection with determining sensing parameters. For example, themorphology, slope, frequency, phase, amplitude and other characteristicsof the respiration data may be used to adjust one or more parameters forthe sensing circuit 144 in connection with sensing CA data. One or moresensing parameters may be continuously updated between successive orgroups of cardiac episodes based on the respiration data.

At 710, the one or more processors utilize the adjusted CA sensingparameters to analyze the CA data to identify events of interest. Forexample, a decreased sensitivity may be used to identify a P-wave,R-wave, ST shift, QRS complex and the like.

FIG. 8 illustrates a system level diagram indicating potential devicesand networks in which the methods and systems herein may be utilized.For example, an implantable cardiac monitoring device (ICM) 802 may beutilized to collect cardiac activity data, obtain respiratory data, andanalyze the respiratory data to determine respiratory COI's and otheroperations in accordance with the methods and systems described herein.The ICM 802 may supply CA data, respiration data, HF monitoring log,adjustments to sensing parameters and the like, to various external andinternal electronic devices, such as a tablet device 804, a smart phone806, a bedside monitoring device 808 and the like. The devices 804-808may perform all or portions of the processing described herein. Forexample, the ICM 802 may convey CA data to one or more of the devices804-808, with the respective device 804-808 performing the remainder ofthe analysis described herein. Additionally or alternatively, the ICM802 may convey the CA data and respiration data to one or more of thedevices 804-808, with the respective device 804-808 performing theremainder of the analysis described herein. The devices 804-808 eachinclude a display to display the various types of information describedherein. The ICM 802 may convey the CA data, respiration data, HFmonitoring log, and the like over various wireless communications linkswith the devices 804, 806 and 808. The ICM 802 may utilize variouscommunications protocols and be activated in various manners. By way ofexample only, when a magnetic device 810 is held next to the patient,the magnetic field from the device 810 may activate the ICM 802 totransmit the cardiac activity data, respiration data, HF monitoring log,etc. to one or more of the devices 804-808.

The processes described herein for analyzing the cardiac activity dataand respiration data may be implemented on the ICM 802, in which casethe event data may then be wirelessly conveyed the HF monitoring logand/or any data related to detection of respiration induced undersensing to one or more of the devices 804-808. Additionally oralternatively, the devices 804-808 may also implement the processesdescribed herein. For example, the ICM 802 may simply convey the rawcardiac activity data for an extended period of time or for discreteperiods of time to one or more the devices 804-810. The devices 804-810then analyze the raw cardiac activity data as described herein, andprovide instructions back to the ICM 802, such as new sensing parametersto be implemented.

The devices 804-808 may present the respiration data, respiration COI's,changes in sensing parameters, changes in event labels and episodeclassifications, the HF monitoring log, etc. to clinicians in variousmanners.

FIG. 9 illustrates a functional block diagram of the external device 900that is operated in accordance with the processes described herein andto interface with ICMs as described herein. The external device 900 maybe a workstation, a portable computer, an ICM programmer, a PDA, a cellphone and the like. The external device 900 includes an internal busthat connects/interfaces with a Central Processing Unit (CPU) 902, ROM904, RAM 906, a hard drive 908, the speaker 910, a printer 912, a CD-ROMdrive 914, an external drive 916, a parallel I/O circuit 918, a serialI/O circuit 920, the display 922, a touch screen 924, a standardkeyboard connection 926, custom keys 928, and a telemetry subsystem 930.The internal bus is an address/data bus that transfers informationbetween the various components described herein. The hard drive 908 maystore operational programs as well as data, such as waveform templatesand detection thresholds.

The CPU 902 typically includes a microprocessor, a micro-controller, orequivalent control circuitry, designed specifically to controlinterfacing with the external device 900 and with the ICM or IMD. TheCPU 902 performs the characteristic of interest measurement processdiscussed above. The CPU 902 may include RAM or ROM memory, logic andtiming circuitry, state machine circuitry, and I/O circuitry tointerface with the ICM or IMD. The display 922 (e.g., may be connectedto the video display 932). The touch screen 924 may display graphicinformation relating to the ICM 900. The display 922 displays variousinformation related to the processes described herein. For example, thedisplay 922 may display the cardiac activity data, as well as additionalinformation as described and illustrated herein. The display 932 (or adisplay on a workstation, phone, personal digital assistant, tabletdevice, etc.) may be configured to display an EGM with markers indictingthe events labeled and classified based on the cardiac activity data.

The touch screen 924 accepts a user's touch input 934 when selectionsare made. The keyboard 926 (e.g., a typewriter keyboard 936) allows theuser to enter data to the displayed fields, as well as interface withthe telemetry subsystem 930. (for example when used in connection with apacemaker) The printer 912 prints copies of reports 940 for a physicianto review or to be placed in a patient file, and speaker 910 provides anaudible warning (e.g., sounds and tones 942) to the user. The parallelI/O circuit 918 interfaces with a parallel port 944. The serial I/Ocircuit 920 interfaces with a serial port 946. The external drive 916accepts an external devices 948 (e.g., USB) or other interface capableof communicating with a USB device such as a memory stick. The CD-ROMdrive 914 accepts CD ROMs 950.

The telemetry subsystem 930 includes a central processing unit (CPU) 952in electrical communication with a telemetry circuit 954, whichcommunicates with both an EGM circuit 956 and an analog out circuit 958.The circuit 956 may be connected to terminals 960. The terminals 960 arealso connected to the implantable or surface electrodes to receive andprocess EGM cardiac signals as discussed above. Optionally, the EGMcardiac signals sensed by the electrodes may be collected by the ICM orIMD and then transmitted, to the external device 900, wirelessly to thetelemetry subsystem 930 input.

The telemetry circuit 954 may be coupled to a telemetry wand 962. Theanalog out circuit 958 includes communication circuits to communicatewith analog outputs 964. The external device 900 may wirelesslycommunicate with the ICM 90 and utilize protocols, such as Bluetooth,GSM, infrared wireless LANs, HIPERLAN, 3G, satellite, as well as circuitand packet data protocols, and the like.

FIG. 10 illustrates a distributed processing system 1000 in accordancewith one embodiment. The distributed processing system 1000 includes aserver 1002 connected to a database 1004, a programmer 1006, a local RFtransceiver 1008 and a user workstation 1010 electrically connected to acommunication system 1012. Any of the processor-based components in FIG.10 (e.g., workstation 1010, cell phone 1014, PDA 1016, server 1002,programmer 1006, ICM 1003) may perform the characteristic of interestmeasurement process discussed above.

The communication system 1012 may be the internet, a voice over IP(VoIP) gateway, a local plain old telephone service (POTS) such as apublic switched telephone network (PSTN), a cellular phone basednetwork, and the like. Alternatively, the communication system 1012 maybe a local area network (LAN), a campus area network (CAN), ametropolitan area network (MAN), or a wide area network (WAM). Thecommunication system 1012 serves to provide a network that facilitatesthe transfer/receipt of information such as cardiac signal waveforms,ventricular and atrial heart rates.

The server 1002 is a computer system that provides services to othercomputing systems over a computer network. The server 1002 controls thecommunication of information such as cardiac activity data, respirationdata, respiration COIs, episode information, markers, cardiac signalwaveforms, ventricular and atrial heart rates, and detection thresholds.The server 1002 interfaces with the communication system 1012 totransfer information between the programmer 1006, the local RFtransceiver 1008, the user workstation 1010 as well as a cell phone 1014and a personal data assistant (PDA) 1016 to the database 1004 forstorage/retrieval of records of information. On the other hand, theserver 1002 may upload cardiac activity data from surface ECG unit 1020or the ICM 1003 via the local RF transceiver 1008 or the programmer1006.

The database 1004 stores information such as cardiac activity data,respiration data, respiration COI, episode information, markers, cardiacsignal waveforms, ventricular and atrial heart rates, detectionthresholds, and the like, for a single or multiple patients. Theinformation is downloaded into the database 1004 via the server 1002 or,alternatively, the information is uploaded to the server from thedatabase 1004. The programmer 1006 is similar to the external device 900and may reside in a patient's home, a hospital, or a physician's office.The programmer 1006 interfaces with (e.g., in connection with apacemaker) the ICM 1003. The programmer 1006 may wirelessly communicatewith the ICM 1003 and utilize protocols, such as Bluetooth, GSM,infrared wireless LANs, HIPERLAN, 3G, satellite, as well as circuit andpacket data protocols, and the like. Alternatively, a telemetry “wand”connection may be used to connect the programmer 1006 to the ICM 1003.The programmer 1006 is able to acquire cardiac signals from the surfaceof a person (e.g., ECGs), electrograms (e.g., EGM) signals from the ICM1003, and/or cardiac activity data, respiration data, respiration COI,episode information, markers, cardiac signal waveforms, ventricular andatrial heart rates, and detection thresholds from the ICM 1003. Theprogrammer 1006 interfaces with the communication system 1012, eithervia the internet, to upload the information acquired from the surfaceECG unit 1020, or the ICM 1003 to the server 1002.

The local RF transceiver 1008 interfaces with the communication system1012 to upload one or more of cardiac activity data, respiration data,respiration COI, episode information, markers, cardiac signal waveforms,and detection thresholds 246 (shown in FIG. 2) to the server 1002. Inone embodiment, the surface ECG unit 1020 and the ICM 1003 have abi-directional connection 1024 with the local RF transceiver 1008 via awireless connection. The local RF transceiver 1008 is able to acquirecardiac signals from the surface of a person, cardiac activity data andother information from the ICM 1003, and/or cardiac signal waveforms,and detection thresholds from the ICM 1003. On the other hand, the localRF transceiver 1008 may download stored cardiac activity data,respiration data, respiration COI, episode information, markers, cardiacsignal waveforms, and detection thresholds, and the like, from thedatabase 1004 to the surface ECG unit 1020 or the ICM 1003.

The user workstation 1010 may interface with the communication system1012 via the internet to download cardiac signal waveforms, ventricularand atrial heart rates, and detection thresholds via the server 1002from the database 1004. Alternatively, the user workstation 1010 maydownload raw data from the surface ECG units 1020, lead 1022 or ICM 1003via either the programmer 1006 or the local RF transceiver 1008. Oncethe user workstation 1010 has downloaded the cardiac signal waveforms,ventricular and atrial heart rates, or detection thresholds, the userworkstation 1010 may process the information in accordance with one ormore of the operations described above. The user workstation 1010 maydownload the information and notifications to the cell phone 1014, thePDA 1016, the local RF transceiver 1008, the programmer 1006, or to theserver 1002 to be stored on the database 1004. For example, the userworkstation 1010 may communicate data to the cell phone 1014 or PDA 1016via a wireless communication link 1026.

The processes described herein in connection with analyzing cardiacactivity data and respiration data may be performed by one or more ofthe devices illustrated in FIG. 10, including but not limited to the ICM1003, programmer 1006, user workstation 1010, cell phone 1014, PDA 1016and server 1002.

Closing

The various methods as illustrated in the Figures and described hereinrepresent exemplary embodiments of methods. The methods may beimplemented in software, hardware, or a combination thereof. In variousof the methods, the order of the steps may be changed, and variouselements may be added, reordered, combined, omitted, modified, etc.Various of the steps may be performed automatically (e.g., without beingdirectly prompted by user input) and/or programmatically (e.g.,according to program instructions).

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended to embrace all such modifications and changes and, accordingly,the above description is to be regarded in an illustrative rather than arestrictive sense.

Various embodiments of the present disclosure utilize at least onenetwork that would be familiar to those skilled in the art forsupporting communications using any of a variety ofcommercially-available protocols, such as Transmission ControlProtocol/Internet Protocol (“TCP/IP”), User Datagram Protocol (“UDP”),protocols operating in various layers of the Open System Interconnection(“OSI”) model, File Transfer Protocol (“FTP”), Universal Plug and Play(“UpnP”), Network File System (“NFS”), Common Internet File System(“CIFS”) and AppleTalk. The network can be, for example, a local areanetwork, a wide-area network, a virtual private network, the Internet,an intranet, an extranet, a public switched telephone network, aninfrared network, a wireless network, a satellite network and anycombination thereof.

In embodiments utilizing a web server, the web server can run any of avariety of server or mid-tier applications, including Hypertext TransferProtocol (“HTTP”) servers, FTP servers, Common Gateway Interface (“CGI”)servers, data servers, Java servers, Apache servers and businessapplication servers. The server(s) also may be capable of executingprograms or scripts in response to requests from user devices, such asby executing one or more web applications that may be implemented as oneor more scripts or programs written in any programming language, such asJava®, C, C# or C++, or any scripting language, such as Ruby, PHP, Perl,Python or TCL, as well as combinations thereof. The server(s) may alsoinclude database servers, including without limitation thosecommercially available from Oracle®, Microsoft®, Sybase® and IBM® aswell as open-source servers such as MySQL, Postgres, SQLite, MongoDB,and any other server capable of storing, retrieving and accessingstructured or unstructured data. Database servers may includetable-based servers, document-based servers, unstructured servers,relational servers, non-relational servers or combinations of theseand/or other database servers.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationmay reside in a storage-area network (“SAN”) familiar to those skilledin the art. Similarly, any necessary files for performing the functionsattributed to the computers, servers or other network devices may bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat may be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (“CPU” or “processor”), atleast one input device (e.g., a mouse, keyboard, controller, touchscreen or keypad) and at least one output device (e.g., a displaydevice, printer or speaker). Such a system may also include one or morestorage devices, such as disk drives, optical storage devices andsolid-state storage devices such as random access memory (“RAM”) orread-only memory (“ROM”), as well as removable media devices, memorycards, flash cards, etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.) and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets) or both. Further, connection to other computing devices suchas network input/output devices may be employed.

Various embodiments may further include receiving, sending, or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-readable medium. Storage media and computerreadable media for containing code, or portions of code, can include anyappropriate media known or used in the art, including storage media andcommunication media, such as, but not limited to, volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage and/or transmission of information suchas computer readable instructions, data structures, program modules orother data, including RAM, ROM, Electrically Erasable ProgrammableRead-Only Memory (“EEPROM”), flash memory or other memory technology,Compact Disc Read-Only Memory (“CD-ROM”), digital versatile disk (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices or any other medium whichcan be used to store the desired information and which can be accessedby the system device. Based on the disclosure and teachings providedherein, a person of ordinary skill in the art will appreciate other waysand/or methods to implement the various embodiments.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims.

Other variations are within the spirit of the present disclosure. Thus,while the disclosed techniques are susceptible to various modificationsand alternative constructions, certain illustrated embodiments thereofare shown in the drawings and have been described above in detail. Itshould be understood, however, that there is no intention to limit theinvention to the specific form or forms disclosed, but on the contrary,the intention is to cover all modifications, alternative constructionsand equivalents falling within the spirit and scope of the invention, asdefined in the appended claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. The term“connected,” when unmodified and referring to physical connections, isto be construed as partly or wholly contained within, attached to orjoined together, even if there is something intervening. Recitation ofranges of values herein are merely intended to serve as a shorthandmethod of referring individually to each separate value falling withinthe range, unless otherwise indicated herein and each separate value isincorporated into the specification as if it were individually recitedherein. The use of the term “set” (e.g., “a set of items”) or “subset”unless otherwise noted or contradicted by context, is to be construed asa nonempty collection comprising one or more members. Further, unlessotherwise noted or contradicted by context, the term “subset” of acorresponding set does not necessarily denote a proper subset of thecorresponding set, but the subset and the corresponding set may beequal.

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. Processes described herein (or variationsand/or combinations thereof) may be performed under the control of oneor more computer systems configured with executable instructions and maybe implemented as code (e.g., executable instructions, one or morecomputer programs or one or more applications) executing collectively onone or more processors, by hardware or combinations thereof. The codemay be stored on a computer-readable storage medium, for example, in theform of a computer program comprising a plurality of instructionsexecutable by one or more processors. The computer-readable storagemedium may be non-transitory.

All references, including publications, patent applications and patents,cited herein are hereby incorporated by reference to the same extent asif each reference were individually and specifically indicated to beincorporated by reference and were set forth in its entirety herein.

It is to be understood that the subject matter described herein is notlimited in its application to the details of construction and thearrangement of components set forth in the description herein orillustrated in the drawings hereof. The subject matter described hereinis capable of other embodiments and of being practiced or of beingcarried out in various ways. Also, it is to be understood that thephraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having” and variations thereof herein ismeant to encompass the items listed thereafter and equivalents thereofas well as additional items.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the inventionwithout departing from its scope. While the dimensions, types ofmaterials and physical characteristics described herein are intended todefine the parameters of the invention, they are by no means limitingand are exemplary embodiments. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Thescope of the invention should, therefore, be determined with referenceto the appended claims, along with the full scope of equivalents towhich such claims are entitled. In the appended claims, the terms“including” and “in which” are used as the plain-English equivalents ofthe respective terms “comprising” and “wherein.” Moreover, in thefollowing claims, the terms “first,” “second,” and “third,” etc. areused merely as labels, and are not intended to impose numericalrequirements on their objects. Further, the limitations of the followingclaims are not written in means-plus-function format and are notintended to be interpreted based on 35 U.S.C. § 102(f), unless and untilsuch claim limitations expressly use the phrase “means for” followed bya statement of function void of further structure.

What is claimed is:
 1. A computer implemented method, comprising: undercontrol of one or more processors, within an implantable cardiac monitor(ICM) to be implanted subcutaneously, configured with specificexecutable instructions, sensing, between two or more electrodes, farfield, subcutaneous electrical cardiac activity (CA) signals generatedby a heart for multiple cardiac cycles, the two or more electrodesconfigured to be located at a site outside of a heart on or adjacent ahousing of the ICM; detecting whether an event of interest is present inthe CA signals; filtering the CA signals to obtain respiration dataindicative of a respiration pattern; and analyzing the respiration datafor respiration induced under detection of the event of interest fromthe CA signals.
 2. The method of claim 1, further comprising classifyingthe cardiac cycle as abnormal cardiac activity when the event ofinterest was not detected as present in the CA signals, the analyzingoperation including confirming or denying abnormal cardiac activitybased on the respiration data.
 3. The method of claim 1, wherein thefiltering and analyzing operations are performed when the detectingoperation fails to detect the event of interest in the CA signals. 4.The method of claim 1, wherein the detecting operation detects whetherthe event of interest is present in the CA signals based on asensitivity threshold, the method further comprising adjusting thesensitivity threshold based on the respiration data.
 5. The method ofclaim 4, wherein the analyzing operation further comprises determiningwhether the respiration COI exceeds a threshold for a predeterminednumber of respiration cycles.
 6. The method of claim 1, wherein theanalyzing operation comprises comparing the respiration data and the CAsignals for respiration induced filtering of the event of interest. 7.An implantable cardiac monitor device, comprising: two or moreelectrodes provided on or adjacent a housing of the ICM, the two or moreelectrodes configured to be implanted at a site outside of the heart; aCA sensing circuit configured to sense, between the two or moreelectrodes, far field, subcutaneous electrical cardiac activity (CA)signals generated by a heart for multiple cardiac cycles, the CA sensingcircuity configured to detect whether an event of interest is present inthe CA signals; a low pass filter (LPF) circuit configured to filter theCA signals to obtain respiration data indicative of a respirationpattern; at least one processor; and a memory coupled to the at leastone processor, wherein the memory stores program instructions, whereinthe program instructions are executable by the at least one processor toanalyze the respiration data for respiration induced under detection ofthe event of interest from the CA signals.
 8. The device of claim 7,wherein the at least one processor is configured to adjust sensingparameters of the CA sensing circuit based on the respiration data. 9.The device of claim 8, wherein the at least one processor is configuredto adjust the sensing parameters by switching between first and secondthresholds applied to the CA signals.
 10. The device of claim 7, furthercomprising a physiologic sensor circuit configured to detect at leastone of activity or posture data, the at least one processor configuredto identify under detection of the event of interest based on the atleast one of activity or posture data, wherein the CA sensing circuitdetects whether the event of interest is present in the CA signals basedon a sensitivity threshold, the at least one processor configured toidentify under detection of the event of interest based on therespiration data, and in response thereto, adjust the sensitivitythreshold.
 11. A computer implemented method, comprising: sensing,between two or more electrodes, far field, subcutaneous electricalcardiac activity (CA) signals generated by a heart for multiple cardiaccycles, the two or more electrodes provided on or adjacent a housing ofan implantable cardiac monitor (ICM) to be implanted subcutaneously, thetwo or more electrodes configured to be located at a site outside of aheart; under control of one or more processors configured with specificexecutable instructions, a) analyzing the CA signals to determine one ormore respiration characteristics of interest (COI); b) collectingsecondary data representing at least one of heart sounds, activity data,posture data or electrogram (EGM) data; c) recording the respirationCOI, information indicative of the secondary data and collection timeinformation concerning when the electrical CA signals and secondary datawere obtained; and repeating the operations at a) to c) to form an HFmonitoring log that includes a collection of respiration COI andinformation indicative of the secondary data over a period of time. 12.The method of claim 11, wherein the a) analyzing further comprisesfiltering the electrical CA signals to obtain respiration dataindicative of a respiration pattern over multiple respiration cycles andanalyzing the respiration data to determine the one or more respirationCOI.
 13. The method of claim 11, further comprising determining, as theinformation, a secondary index based on secondary data, the HFmonitoring log including a collection of the secondary indices of theperiod of time.
 14. The method of claim 13, further comprising analyzingat least one of the collection of respiration COI or secondary indexover the period of time with respect to a baseline; and generating anindicator of potential heart failure based on a relation between the atleast one of the collection of respiration COI or secondary index andthe baseline.
 15. The method of claim 11, further comprising providing acorrective action based on the HF monitoring log.
 16. The method ofclaim 15, wherein the corrective action includes at least one ofconsulting a physician or make a change to a medication.
 17. The methodof claim 11, wherein the collecting the secondary data includescollecting heart sound data.
 18. The method of claim 11, wherein the HFmonitoring log includes information related to at least one of heartrate, heart rate variability, episode-related diagnostics, episodeduration, episode count, episode date and time stamp or heart ratehistogram or atrial fibrillation.
 19. A system, comprising: animplantable cardiac monitor (ICM) configured to be implanted at a siteoutside of the heart; two or more electrodes provided on or adjacent ahousing of the ICM, the two or more electrodes configured to beimplanted at the site outside of the heart; at least one processor; anda memory coupled to the at least one processor, wherein the memorystores program instructions, wherein the program instructions areexecutable by the at least one processor to: a) sense, between the twoor more electrodes, far field, subcutaneous electrical cardiac activity(CA) signals generated by a heart for multiple cardiac cycles; b)analyze the CA signals to determine one or more respirationcharacteristics of interest (COI); c) collect secondary datarepresenting at least one of heart sounds, activity data, posture dataor electrogram (EGM) data; d) record the respiration COI, informationindicative of the secondary data and collection time informationconcerning when the electrical CA signals and secondary data wereobtained; and repeat the operations at a) to d) to form an HF monitoringlog that includes a collection of respiration COI and informationindicative of the secondary data over a period of time.
 20. The systemof claim 19, wherein the processor is further configured to analyze theCA signals by filtering the electrical CA signals to obtain respirationdata indicative of a respiration pattern; and analyze the respirationdata to determine the one or more respiration COI.
 21. The system ofclaim 19, wherein the processor is further configured to determine, asthe information, a secondary index based on secondary data, the HFmonitoring log including a collection of the secondary indices of theperiod of time.
 22. The system of claim 19, wherein the ICM includes ahousing that encloses the memory and the at least one processor, the ICMincluding a transceiver to wirelessly transmit the HF monitoring log toan external device.
 23. The system of claim 19, wherein the HFmonitoring log includes information related to at least one of heartrate, heart rate variability, episode-related diagnostics, episodeduration, episode count, episode date and time stamp or heart ratehistogram or atrial fibrillation.
 24. The system of claim 19, whereinfurther comprising an external device that houses at least a portion ofthe memory and an external processor from the at least one processor,the ICM including a transceiver to wirelessly transmit the CA signals tothe external device, the external device configured to implement theanalyze and record operations to form the HF monitoring log.
 25. Thesystem of claim 24, wherein the external device is further configured tocollect the secondary data, separate from the ICM, and record theinformation indicative of the secondary data.